Effective Statistical Learning Methods for Actuaries II, 1st ed. 2020 Tree-Based Methods and Extensions Springer Actuarial Lecture Notes Series
This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities.
The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful.
This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance.
Provides an exhaustive and self-contained presentation of tree-based methods applied to insurance
Gives a rigorous statistical analysis of tree-based methods
Fills a gap in the literature on artificial intelligence techniques applied to insurance
Written by actuaries for actuaries
Based on more than a decade of lectures and consulting projects on the topic, by the three authors
Offers several case studies in P&C insurance
Date de parution : 11-2020
Ouvrage de 228 p.
15.5x23.5 cm